Digital education governance: data visualization, predictive analytics, and ‘real-time’ policy instruments
By Ben Williamson. 2016
414 citations recorded by the source corpus.
Educational institutions and governing practices are increasingly augmented with digital database technologies that function as new kinds of policy instruments. This article surveys and maps the landscape of digital policy instrumentation in education and provides two detailed case studies of new digital data systems. The Learning Curve is a massive online data bank, produced by Pearson Education, which deploys highly sophisticated digital interactive data visualizations to construct knowledge about education systems. The second case considers ‘learning analytics’ platforms that enable the tracking and predicting of students’ performances through their digital data traces. These digital policy instruments are evidence of how digital database instruments and infrastructures are now at the centre of efforts to know, govern and manage education both nationally and globally. The governing of education, augmented by techniques of digital education governance, is being distributed and displaced to new digitized ‘centres of calculation’, such as Pearson and Knewton, with the technical expertise to calculate and visualize the data, plus the predictive analytics capacities to anticipate and pre-empt educational futures. As part of a data-driven style of governing, these emerging digital policy instruments prefigure the emergence of ‘real-time’ and ‘future-tense’ techniques of digital education governance.
Subfields: Computer Science Applications, Information Systems, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/5a97b45717e1d1bc4fe6da1c554c598f23680374
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Citations in the corpus
- What Do We Talk About When We Talk About Dashboards? (2019, background): “Such constrained “scorecard” views and quantitative models amenable to traditional data analytics can impose mechanistic, unsophisticated decision-making regimes [3, 24, 29, 35, 64] that are susceptible to misinterpretation.”